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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Jan 2020

21 Jan 2020

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This preprint is currently under review for the journal GMD.

Reduced complexity model intercomparison project phase 1: Protocol, results and initial observations

Zebedee R. J. Nicholls1,2, Malte Meinshausen1,2,3, Jared Lewis1, Robert Gieseke3, Dietmar Dommenget4, Kalyn Dorheim5, Chen-Shuo Fan4, Jan S. Fuglestvedt6, Thomas Gasser7, Ulrich Golüke8, Philip Goodwin9, Elmar Kriegler3, Nicholas J. Leach10, Davide Marchegiani4, Yann Quilcaille7, Bjørn H. Samset6, Marit Sandstad6, Alexey N. Shiklomanov5, Ragnhild B. Skeie6, Christopher J. Smith11, Katsumasa Tanaka12,13, Junichi Tsutsui14, and Zhiang Xie4 Zebedee R. J. Nicholls et al.
  • 1Australian–German Climate and Energy College, The University of Melbourne, Parkville, Victoria, Australia
  • 2School of Earth Sciences, The University of Melbourne, Parkville, Victoria, Australia
  • 3Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
  • 4Monash University, School of Earth, Atmosphere and Environment, Clayton, Victoria 3800, Australia
  • 5Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA
  • 6CICERO Center for International Climate Research, Oslo, Norway
  • 7International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
  • 8BI Norwegian Business School, Nydalsveien 37, 0484 Oslo, Norway
  • 9School of Ocean and Earth Science, University of Southampton, Southampton, UK
  • 10Department of Physics, Atmospheric Oceanic and Planetary Physics, University of Oxford, UK
  • 11Priestley International Centre for Climate, University of Leeds, UK
  • 12National Institute for Environmental Studies (NIES), Tsukuba, Japan
  • 13Laboratoire des Sciences du Climat et de l’Environnement (LSCE), Commissariat à l’énergie atomique et aux énergiesalternatives (CEA), Gif sur Yvette, France
  • 14Central Research Institute of Electric Power Industry, Abiko, Japan

Abstract. Here we present results from the first phase of the Reduced Complexity Model Intercomparison Project (RCMIP). RCMIP is a systematic examination of reduced complexity climate models (RCMs), which are used to complement and extend the insights from more complex Earth System Models (ESMs), in particular those participating in the Sixth Coupled Model Intercomparison Project (CMIP6). In Phase 1 of RCMIP, with 14 participating models namely ACC2, AR5IR (2 and 3 box versions), CICERO-SCM, ESCIMO, FaIR, GIR, GREB, Hector, Held et al. two layer model, MAGICC, MCE, OSCAR and WASP, we highlight the structural differences across various RCMs and show that RCMs are capable of reproducing global-mean surface air temperature (GSAT) changes of ESMs and historical observations. We find that some RCMs are capable of emulating the GSAT response of CMIP6 models to within a root-mean square error of 0.2 °C (of the same order of magnitude as ESM internal variability) over a range of scenarios. Running the same model configurations for both RCP and SSP scenarios, we see that the SSPs exhibit higher effective radiative forcing throughout the second half of the 21st Century. Comparing our results to the difference between CMIP5 and CMIP6 output, we find that the change in scenario explains approximately 46 % of the increase in higher end projected warming between CMIP5 and CMIP6. This suggests that changes in ESMs from CMIP5 to CMIP6 explain the rest of the increase, hence the higher climate sensitivities of available CMIP6 models may not be having as large an impact on GSAT projections as first anticipated. A second phase of RCMIP will complement RCMIP Phase 1 by exploring probabilistic results and emulation in more depth to provide results available for the IPCC's Sixth Assessment Report author teams.

Zebedee R. J. Nicholls et al.

Interactive discussion

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Zebedee R. J. Nicholls et al.

Data sets

RCMIP Phase 1 Data Z. R. J. Nicholls, M. Meinshausen, J. Lewis, R. Gieseke, D. Dommenget, K. Dorheim, C.-S. Fan, J. S. Fuglestvedt, T. Gasser, U. Golüke, P. Goodwin, E. Kriegler, N. J. Leach, D. Marchegiani, Y. Quilcaille, B. H. Samset, M. Sandstad, A. N. Shiklomanov, R. B. Skeie, C. J. Smith, K. Tanaka, J. Tsutsui, and Z. Xie

Zebedee R. J. Nicholls et al.


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Publications Copernicus
Short summary
Computational limits mean that we cannot run our most comprehensive climate models for all applications of interest. In such cases, reduced complexity models (RCMs) are used. Here researchers working on 14 different models present methods and results from the first community effort to evaluate and compare RCMs. Our research ensures that users of RCMs, such as the authors of the IPCC's Sixth Assessment Report, can more easily evaluate the strengths, weaknesses and limitations of their tools.
Computational limits mean that we cannot run our most comprehensive climate models for all...